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Record W2531687773 · doi:10.1021/acs.jpcb.6b08798

Solid-State <sup>17</sup>O NMR of Unstable Acyl-Enzyme Intermediates: A Direct Probe of Hydrogen Bonding Interactions in the Oxyanion Hole of Serine Proteases

2016· article· en· W2531687773 on OpenAlexafffund
Aaron W. Tang, Xianqi Kong, Victor V. Terskikh, Gang Wu

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2016
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of OttawaQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryOxyanion holeHydrogen bondOxyanionStereochemistryChemical shiftMoietyCrystallographyNuclear magnetic resonance spectroscopyMoleculeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

We report preparation, trapping, and solid-state 17 O NMR characterization of three unstable acyl-enzyme intermediates (≈ 26 kDa): p - N, N -dimethylamino-[ 17 O]benzoyl-chymotrypsin, trans - o -methoxy-[ 17 O]cinnamoyl-chymotrypsin, and trans - p -methoxy-[ 17 O]cinnamoyl-chymotrypsin. We show that both the 17 O chemical shifts and nuclear quadrupolar parameters obtained for these acyl-enzyme intermediates in the solid state are correlated with their deacylation rate constants measured in aqueous solution. With the aid of quantum mechanical calculations, the experimental 17 O NMR parameters were interpreted as to reflect the hydrogen bonding interactions between the carbonyl (C═ 17 O) functional group of the acyl moiety and the two NH groups from the protein backbone (Ser195 and Gly193) in the oxyanion hole, a general feature of all serine proteases. Our results further suggest that the 17 O chemical shift and quadrupole coupling constant display distinctly different sensitivities toward different aspects of hydrogen bonding, such as hydrogen bond distance and direction. This work demonstrates the utility of 17 O as a useful nuclear probe in NMR studies of enzymes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.302
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2016
Admission routes2
Has abstractyes

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